Overview

Cat is a health data scientist with an interdisciplinary natural science background. In her PhD she uses machine learning to help patients with type 1 diabetes achieve euglycaemia during exercise.

Cat is driven by both curiosity and a desire to see data science used as a power for good in our communities. Her strengths include an ability to explain complex ideas simply and thriving in a multidisciplinary teams.

In her free time, Cat can be found roaming the mountains, reading a good book or nattering with friends in the pub!

Qualifications

• BSc Liberal Arts and Science, University of Maastricht
• MSc Computer Science, University of East Anglia

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Research

Research interests

Health data science

Interpretable machine learning

Python

Open-source code

Public health

Data for good

Research projects

• Interpolation of 15-minute CGM data to improve the identification of hypoglycaemic episodes
• Machine learning to improve euglycaemia during exercise
• Neural networks to predict overnight hypoglycaemia

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